Max-Min Rate Optimization for Uplink IRS-NOMA With Receive Beamforming

This letter addresses an intelligent reflecting surface (IRS) to the uplink nonorthogonal multiple access (NOMA) served by a multiantenna receiver for effective data collection from massive devices. We aim achieve max-min fairness of network optimizing receive beamforming, IRS reflection, and transmit power allocation (PA) For this purpose, first, we design block coordinate descent (BCD) algorithm that reduces complexity conventional reflection optimization. Next, nonlinear optimization (NLO) problem solvable with limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which is renowned handling large-scale problems, cope large elements The formed smooth but complex objective function depends on phase shift PA vectors gradient derived in computationally efficient form. results reveal proposed BCD NLO L-BFGS-B outperform performance complexity, where approach offers substantial reduction.